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https://dev.mysql.com/doc/heatwave/en/mys-hw-string-functions-operators.html
With the exception of the FORMAT() function, string functions and operators described in the following table are supported with variable-length encoded columns. FIELD() Index (position) of first argument in subsequent arguments FIND_IN_SET() Index ...
https://dev.mysql.com/doc/heatwave/en/mys-hwaml-anomaly-detection-logs.html
The input table can only have the following columns: The column containing the logs. As of MySQL 9.4.1, at least one column must act as the primary key to establish the temporal order of logs. If the input table has additional columns to the ones ...
https://dev.mysql.com/doc/heatwave/en/mys-hwaml-explanations-ml-explain-row.html
The column names must match the feature column names in the trained table. mysql> CALL sys.ML_EXPLAIN ('table_name', 'target_column_name', model_handle, [options]); The following example runs the shap explainer. mysql> CALL ... ML_EXPLAIN_ROW ...
https://dev.mysql.com/doc/heatwave/en/mys-hwaml-explanations.html
Prediction explanations are generated by running ML_EXPLAIN_ROW or ML_EXPLAIN_TABLE on unlabeled data. You can train the following prediction explainers: The Permutation Importance prediction explainer, specified as permutation_importance, is the ...The data must have the same feature columns as the data used to train the ...
https://dev.mysql.com/doc/heatwave/en/mys-hwaml-limitations.html
Memory Limitations The table used to train a model cannot exceed 10 GB, 100 million rows, or 1017 columns. Routine and Query Limitations ML_EXPLAIN_TABLE and ML_PREDICT_TABLE are compute intensive processes, with ML_EXPLAIN_TABLE being the most ...
https://dev.mysql.com/doc/heatwave/en/mys-hwaml-ml-explain.html
To run ML_EXPLAIN_ROW and ML_EXPLAIN_TABLE with a different explainer, you must first run ML_EXPLAIN with the same explainer. Required ML_EXPLAIN Parameters Set the following required parameters: table_name: You must define the table that you ...
https://dev.mysql.com/doc/heatwave/en/mys-hwaml-model-handles.html
If you do not specify a model handle name, a model handle is automatically generated that is based on the database name, input table name, the user name training the table, and a unique numerical identifier. The following example queries the model ... When ML_TRAIN trains a model, you have the option to specify a name for the model, which is the model ...
https://dev.mysql.com/doc/heatwave/en/mys-hwaml-model-viewing.html
To view the details for the models in your model catalog, query the MODEL_CATALOG table. Before You Begin Review the following: Create a Machine Learning Model The Model Catalog View Details for Your Models The following example queries model_id, ...View Model Explanations The ML_EXPLAIN routine generates model explanations and stores them in the model ...
https://dev.mysql.com/doc/heatwave/en/mys-hwaml-onnx-import-overview.html
You cannot directly load models in ONNX format (.onnx) into a MySQL table. MySQL HeatWave AutoML supports the following ONNX model types: An ONNX model that has only one input, and it is the entire MySQL table. An ONNX model that has more than one ...The models require string serialization and conversion to Base64 encoding before you use the ML_MODEL_IMPORT ...
https://dev.mysql.com/doc/relnotes/heatwave/en/news-9-0-1-u1.html
(WL #16399) The MySQL HeatWave GenAI ML_RAG routine now includes new filtering options, exclude_vector_store and exclude_document_name, which let you exclude specific vector store tables or documents from context retrieval. (WL #16401) You can now ... MySQL HeatWave AutoML MySQL HeatWave GenAI MySQL HeatWave Lakehouse MySQL HeatWave MySQL HeatWave AutoML MySQL HeatWave AutoML now supports semi-supervised learning for anomaly ...